Python：使用多处理池时使用队列写入单个文件

Question

我有数十万个文本文件，我想以各种方式对其进行解析。我想将输出保存到单个文件而不会出现同步问题。我一直在使用多处理池来执行此操作以节省时间，但我不知道如何组合池和队列。

以下代码将保存文件名以及文件中连续“x”的最大数量。但是，我希望所有进程都将结果保存到同一个文件中，而不是像我的示例中那样保存到不同的文件中。对此的任何帮助将不胜感激。

import multiprocessing

with open('infilenamess.txt') as f:
    filenames = f.read().splitlines()

def mp_worker(filename):
 with open(filename, 'r') as f:
      text=f.read()
      m=re.findall("x+", text)
      count=len(max(m, key=len))
      outfile=open(filename+'_results.txt', 'a')
      outfile.write(str(filename)+'|'+str(count)+'\n')
      outfile.close()

def mp_handler():
    p = multiprocessing.Pool(32)
    p.map(mp_worker, filenames)

if __name__ == '__main__':
    mp_handler()

Answer 1

多处理池为您实现了一个队列。只需使用将工作人员返回值返回给调用者的池方法。 imap 运行良好：

import multiprocessing 
import re

def mp_worker(filename):
    with open(filename) as f:
        text = f.read()
    m = re.findall("x+", text)
    count = len(max(m, key=len))
    return filename, count

def mp_handler():
    p = multiprocessing.Pool(32)
    with open('infilenamess.txt') as f:
        filenames = [line for line in (l.strip() for l in f) if line]
    with open('results.txt', 'w') as f:
        for result in p.imap(mp_worker, filenames):
            # (filename, count) tuples from worker
            f.write('%s: %d\n' % result)

if __name__=='__main__':
    mp_handler()

Answer 2

我接受了已接受的答案并对其进行了简化，以了解其工作原理。我把它贴在这里以防它帮助别人。

import multiprocessing

def mp_worker(number):
    number += 1
    return number

def mp_handler():
    p = multiprocessing.Pool(32)
    numbers = list(range(1000))
    with open('results.txt', 'w') as f:
        for result in p.imap(mp_worker, numbers):
            f.write('%d\n' % result)

if __name__=='__main__':
    mp_handler()

Answer 3

这是我使用多处理管理器对象的方法。这种方法的好处是，当处理退出管理器并在 run_multi() 函数中使用块时，文件写入器队列会自动关闭，从而使代码非常易于阅读，并且您无需尝试停止侦听队列。

from functools import partial
from multiprocessing import Manager, Pool, Queue
from random import randint
import time

def run_multi():
    input = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    with Manager() as manager:
        pool = Pool()  # By default pool will size depending on cores available
        message_queue = manager.Queue()  # Queue for sending messages to file writer listener
        pool.apply_async(file_writer, (message_queue, ))  # Start file listener ahead of doing the work
        pool.map(partial(worker, message_queue=message_queue), input)  # Partial function allows us to use map to divide workload

def worker(input: int, message_queue: Queue):
    message_queue.put(input * 10)
    time.sleep(randint(1, 5))  # Simulate hard work

def file_writer(message_queue: Queue):
    with open("demo.txt", "a") as report:
        while True:
            report.write(f"Value is: {message_queue.get()}\n")

if __name__ == "__main__":
    run_multi()